Donald Clark Plan B

Monday, March 19, 2018

AI has and will change language learning forever

Just before the dawn of the internet I worked with the CEO of a major CD language learning company. His business model was fascinating,

“I don’t sell language learning, I sell the false promise…. My customers are ‘false starters’ mostly middle-class people who think they’ll learn a language in a few months before they go to Italy, Spain or France on their holidays… they never do.” He explained that the whole market was based on this model. The BBC packages at the time were the worst, he explained, “They’d send a film team to France for a month or so, come back and write a book around it…. it is literally impossible to learn a language from their materials”. I stayed out of that market. But times they are a changin’….

Internet

The technology moved on from CDs, along came the internet, and we saw the first big effect on learning languages – mainly English. The abundance of music, films, sport in all media on the web, allowed ready access to content, allowing contact, practice and immersion. Huge numbers have learnt languages without direct instruction. But learning a second language remains one of the most difficult things one can do in life and direct instruction still has a place. The problem with online instruction is that the technology was still too flat, text based and restricted to simple drill and practice. The content was too linear, often dull and struggled when it came to the spoken word, practice and immersion. Technology is now influencing not only what languages we learn but how and even why we learn languages. Some argue that the Anglo-saxon domination of the internet has accelerated the expansion of English as a global language. Machine translation raises the interesting possibility in that it may lead to less people learning new languages, if frictionless, real-time translation is available. But the most obvious and immediate impact will be on the practical teaching and learning of languages, where smart technology is already having a global impact.

AI

Of one thing we can be sure; AI brings a new paradigm to language learning. Natural Language Processing (NLP) has brought entity analysis, sentiment analysis, classification and machine translation. In addition we have text to speech and speech to text, now revolutionising interfaces. At the same time algorithmic techniques and machine learning brought adaptive, personalised and spaced learning. Even image recognition is being brought into identification and assessment. These technologies are being blended to produce sophisticated language learning and the possibility of learning a language without human instruction. One has to look across the whole learning journey to see how this is potentially possible.

Machine learning

To see how far AI has come in languages, Machine Translation is a good starting point . Google Translate can handle over 100 languages and us used over half a billion times a day. Launched in 2006 it used Statistical Machine Translation to match strings by probability against strings in another language, basically pattern matching. But in late 2016 it switched to Neural Machine Translation, making it much more successful and contextual. It is available as a browser extension and on Google Home and Google’s Pixel Buds. The ear ‘Buds’ can translate 40 languages in real time. To be fair, like Skype’s real time translation, it’s far from fluid and perfect but the direction of travel is clear – it will get better and better.

Learning journey

So what about learning a language? Most successful language learning models take the learner on a learning journey from simple basics to practice then production. This progression normally starts with structural basics on the alphabet, vocabulary and grammar. Practice usually starts with limited and controlled practice and moves towards more open and free practice. Finally, there is generative production and use of the language. In addition to the actual learning there are also pedagogic issues such as motivation (a particular problem in language learning) and assessment. AI has a role to play across the whole of this learning journey.

Drill and practice

My first ever computer-based learning programme was teaching the Russian alphabet, which I built using the Commodore 64 graphic characters. You saw a character and had to type in the corresponding English sound (as a letter or letters). I then programmed a behavioural drill and practice vocabulary programme. Randomisation was a feature, stratified with progress dependent on scores. This was typical of most early computer assisted language learning programmes.

Adaptive learning

Basic drill and practice is still a feature of most adaptive systems, such as Duolingo, with 200 million registered users, where structured topics are introduced, alongside basic grammar but adaptive algorithmic techniques track your progress and take into consideration, your forgetting curve, short-term success rate and effort. Adaptive systems can blend individual with aggregate data to optimise progress for the learner, depending on need. Every new learning event can be uniquely presented to that learner thus personalising the learning, an important form of optimisation in language learning, give the distribution of ability.

Spaced practice

Spaced practice, where the learners use retrieval techniques in a structured reinforcement pattern to push knowledge and skills from working to long term memory is a good starting point for the consolidation of acquired knowledge and skills. Anki is a free package that uses the algorithmic control of spaced practice to determine the learning path.

Chatbots

Controlled practice, to varying degrees, can also be delivered using chatbots. There are many species of chatbots from learning engagement, teaching, mentorbots, and practice bots. Chat has overtaken social media on mobiles and is clearly the preferred interface. We seem to have a natural affinity to chat interfaces and in some cases, with wellbeing bots, even the anonymity of the machine has been shown to be an advantage. They have been successfully used in educational and corporate training environments. They offer a dialogue interface, so are eminently suitable for language learning, with flexibility around the recognition of replies by the learner and, of course, speech. They have huge potential and when embodied in consumer, home devices can bring language learning into to the home.

Open practice

But active immersion is also now possible with home devices. You can switch your Amazon Echo to respond in German. Consumer technology, such as Alexa, Google Home and others will offer cheap, free and increasingly sophisticated language learning in your home. Ask it a question in English and it will reply in German. This is a bit like having a German person in your own home 24/7.

Immersion

The internet provides a wide and deep set of resources in most major languages. There’s an endless amount of content in your target language, in all media – text, audio and video - movies, box sets, music videos, Youtube, Wikipedia, whatever. Here other immersive technologies come into play, such as VR and AR. These are not AI technologies but AI techniques can be used within these environments to provide immersion, attention and context for language learning. In a current project (WildFire) we have successfully integrated speech input within VR, which not only allows you to navigate through the learning using just your voice but also input open response input and so on.

Assessment

Both Babbel and Doulingo offer paid English assessment testing. Face and digital recognition allow unique identification of candidates for assessment. Keyboard typing patterns can be recognised, along with adaptive assessment, which adapts to the candidate’s ability level, are all being used. Online assessment is now here, which increases accessibility and progress in language learning.

Conclusion

AI, with its rapid advances, specifically in technologies that aid language learning, may turn out to be the most significant technology in this field to date. The technology provides behind the scenes language processing that allows machine translation, speech recognition and many other services to be used across the learning journey to keep learners moving forward, optimising and personalising delivery. It has already accelerated the digitisation, disintermediation, decentralisation and democratisation of language learning. Yet we must be careful in attributing too much efficacy to AI. Its translation ability is nowhere near as good as human translation, speech recognition still a bit ropey and with other services, such as chatbots you need to be a bit forgiving. Nevertheless, it is constantly improving and on current rates of progress, it seems likely that it will have a major impact in language learning.

Thursday, March 15, 2018

Should you listen to music while studying? No... here's why

There are
those who extoll the Mozart Effect, I know of one who extolled the virtues of
playing Mozart to her children when they were very young and when they were
learning. This, she claimed, had been proved scientifically to improve IQ and
their ability to retain knowledge. Remarkably, she extended her claim to the foetus.

This
baloney was sparked off by a paper in Nature by Rauscher, Shaw and Ky (1993),
which showed a small improvement in spatial reasoning score (very specific),
the effect lasted no longer than 15 minutes, then disappeared. The theory also
disappeared, as several follow up studies could not replicate the effect.
Rauscher herself, disclaimed the idea, saying that they had made no claim
linking the playing of Mozart to intelligence. Chabris and Steele in a
meta-studies paper in 1999 put the nail in the coffin by showing that such
effects are merely the result of short-term and temporary ‘enjoyment arousal'.

But
education can never resist a fad and there's always someone in education who can't let a bandwagon pass in this case Don Campbell, who published
The Mozart Effect (1997) and The Mozart Effect for Children. These books are,
quite simply, bogus. His claims bear no resemblance to the actual research and,
if you have this idea floating around in your brain, it’s largely down to him
trade-marking the effect, then publishing these books, that were then taken up
by lazy ill-informed journalists. This is how it ended up in the minds of so many
parents and teachers. It was even funded and applied in some states in the US,
notably Georgia and Florida.

Music in general

On the
general proposition, that listening to music helps one learn, we have to be as
equally careful. There is a large and complex literature on this subject,
testing the effect of music on various cognitive phenomena and there is some
evidence that it improves mood, even motivation, but one must be careful when
it comes to actual learning.

In this interesting study, silence is used as a control, along with the two major
components in popular music - music and lyrics. Perham and Currie (2014) created
four groups:

Silence

Music without lyrics

Music with
lyrics they liked

Music with
lyrics they did not like

ResultsThe sample (30) was small, and I'd like to see this replicated with a larger group but the results were interesting:

Revising in silence was signifiantky better than revising while listening to music that had lyrics, liked or dislikedNo difference between 'silence' and 'music with no lyrics'

Revising while listening to 'music without lyrics' was better than revising to 'music with lyrics'

Students
who revised in 'silence' were the best at predicting the results

Why?

It's to do with the overloading of working memory, especially with spoken words. One quick
experiment you can do with your kids, or students, is to take a random page from
a book on a subject they are unfamiliar with. Now tell them to read it in silence.
Now choose another page and ask them to read it while repeating the word ‘boing-boing’
over and over. They will be unable to meaningfully learn from the text. The reason
is the overloading of working memory, the phonological loop to be exact. Music takes up valuable bandwidth, therefore inhibits learning.

Conclusion

It may be
devilishly difficult to convince your offspring that music is bad when they’re
studying but when faced with a 60% differential it may be worth telling them
about this study. There is lots of bad advice around study techniques that focus on superficial, low retention study methods and ignore attention, effort,
retrieval and deliberate practice. No doubt some wag will tell us that music is
good for those with an auditory learning style... that's also bullshit.

Monday, March 05, 2018

Why is online learning ‘all fur coat and no knickers’? We design to forget.

Online learning has gone down the ‘all fur coat and no
knickers’ route. It’s more presentation than pedagogy, more look and feel than
learning. Rather than focus on what makes learning a success in terms of understanding, retention and recall, it allows the learner to skate across the surface of a
thin layer of crisply designed but thin ice. It often creates the illusion of
learning by illustrative graphics/animation that, as Mayer often showed, actually
inhibit rather than help retention. That old adage, which is as good a summary
of learning theory as any, that ‘less is more’, has been abandoned by a glut of
over-engineered graphics, animation and effects. We design for forgetting.

Google

Rather than taking our lead from the most successful online services
the world has ever seen, such as Google, which has the simplest, most
successful and most alluring interface ever seen, we wallow in an agency-model
that delivers a diet of over-designed cartoons, stock images, animation,
badges, gamification and every other damn distraction we can think of. ‘Keep It
Simple Stupid’ has been replaced by ‘Keep It Stupid Stupid’. Google focus on
back-end functionality to deliver a superb service, not front-end visuals. So should we.

Amazon

Take the world’s most successful retailer, Amazon, with 44%
of all online sales. They are obsessed with customer behaviour and
simplification – not aesthetic design. Their website could be described as
quite ugly, but it’s a masterpiece of cognitive simplification and the design
of process and success, not aesthetic ‘look and feel’. They are successful
because aesthetic design isn’t the point – selling and buying is the point.
Similarly in learning – teaching and learning is the point. Like Google, they focus on back-end functionality to deliver a
superb service, and do not rely on front-end visuals.

Social media

One could hardly describe Facebook and Twitter as relying on
their designed interface or images for success. There are no Facebook or twitter
images, there is no animation, only a core, scrolling timeline that draws you
in and a simple interface that gets you typing stuff in. They understand that
the goal is interaction, not spoon feeding, that the software behind the skin
is where the real power lies. They understand that less is more.

Successful learning
design

So how should we design for success in learning? First up,
we need to focus on the outcome – successful retention and recall. This is our
equivalent of Google's ‘finding the right thing' so that we click on it’ or
Amazon’s ‘offering us the right thing so we buy it’ and Facebook/Twitter’s
‘interaction with others’. This comes down to a few simple principles:

1. Effortful design

Forget the graphic/text/graphic/text/MCQ model for one
moment and think about the simple fact that the learner really does need to
make the cognitive effort to learn.
You have to make them think and act. The online learning industry is obsessed
with the MCQ and their awkward cousins, the T/F, drag and drop and so on. Multiple
choice questions are light touch, give the answer anyway and are poor on
retention. That is because they are weak in terms of effort. You are not making
the learner recall the answer from their own brains, rather, they are choosing
from a list. It's an act of recognition. These interactions bear no similarity to how people actually use
what they learn in real life. You have to know stuff, recall stuff, not pick
stuff from lists or drag words from one place to another. If you don’t you’re
designing for forgetting. So move to open input.

2. Simplicity

Google, Amazon, Twitter, Facebook, Netflix and every other
online service, allows you to scroll down the page. They have largely abandoned
the online learning, fixed-page model. Most online learning vendors have
scrolling on their own websites but when it comes to learning design they default back
to some old-school, fixed-page turning model. Sure you need to chunk material down but electronic
page turning through coffee-book designed pages, is not the answer. No need to be flashy, Flash died for a good reason. You
need to cut things down, get rid of those extraneous graphics – those stock
photos of people in offices, looking at computer screens, managers smiling
inanely at each other., patronising cartoons.... You also need to cut the text until it bleeds, then cut
it some more. A good editor is of more use than a graphics designer. Forget
those dull learning objectives at the start of your course, all of that
Michelin-man padding. Sure, adhere to some simple rules on branding, through
logo, palette and font – that usually means pre-defined colours but don’t get
fixated on superfluous elements that distract. Your goal is learning and
retention not aesthetic pleasure.

3. Get smart

Stuck in a flat HTML world where all the effort goes into
page design and a flashy CSS, the online learning world hasn’t learnt from
Google, Amazon, Facebook, Twitter and Netflix. AI is the new UI. As all the effort goes into the
surface skin, there is no smart delivery behind the front-end. Google is pure
AI, Amazon’s huge AI platform delivers what you see with subtle recommendations
based on your personal behaviour and the behaviour of others. Social media is
mediated by AI as is Netflix, which is why it has conquered the globe in the
entertainment industry. Yet in online learning we are stuck with flat pages of
HTML, with a few branches. Look at AI, that is now the real world.

Conclusion

We are in this pickle because we do not pay enough attention
to learning theory. Anyone can say ‘that looks nice’ few can say ‘that’s great
learning’ and justify their claim. What to do? Let’s get smart by using smart,
behind the scenes software to drive the delivery of online learning. Let’s be
honest and say that what we had was OK for that time but it’s time to move on.
Let’s drop the idea that it’s all about ‘design’ and focus on functionality and
leaning outcomes – what we actually retain and recall. Let’s stop being a
nation of online shop, window-dressers and focus on learning, which is why we
need newer tools and services, that can deliver effortful learning and work to
principles of cognition that lead to learning not just looking.

If you're interested in this direction contact us on WildFire - the world's first AI-driven content creation tool. Or try a different approach.... adios....

Tuesday, February 20, 2018

We don’t need no stinkin’ badges? Why the badges movement has literally run its course

I’d have loved the idea of learning badges to have worked – motivational
dynamo, more fine-grained rewards and accreditation. The inconvenient truth is
that the idea has failed. This is not for want of trying but a classic case of
supply not matched by demand. To put it another way, we built it and they
didn’t come. Sure you’ll find some localised examples of success but overall,
as a significant movement, it has literally run its course - few are now
interested.

1. Lack credibility

The main problem has been credibility. When explicit accreditation is not anchored in a major
accreditation body with quality and standards, there’s no real anchor in the
real world. You’re up against recognised accreditation with branding,
marketing, frameworks, objective assessment and longevity. Overbadging and weak
badging have added to the problem of credibility. Badge projects are here today
gone tomorrow, mosquitos not turtles.

2. Lack objectivity

A lack of objectivity,
in terms of recognition in the real word has plagued their progress. What
happens when you take your badges outside of your institution or course, and no
one has ever heard of them and don’t care? Simply badging content is a mistake.
This is about real people feeling that they are useful, not lapel badges. If
your currency is not recognised in the currency exchange, then you’re left with
useless paper.

3. Motivationally
suspect

They were always motivationally
suspect. Extrinsic rewards should always be treated with suspicion. And
there’s something suspect about badges for online, but not offline, stuff. You
can’t slice and dice learning by mode of delivery. The ‘Overjustification
effect’ shows that Intrinsic motivation will decrease when external
rewards are only given for completing a particular task or only doing minimal work.
This is not to say that all extrinsic motivation is useless, only that
superfluous extrinsic motivation is damaging to learning. The failure to escape
this trap is a major problem for most badge schemes.

4. Not really
gamification

The idea that they are a great
gamification feature is misleading. Pavlovian rewards have a limited effective learning, which is why so much Pavlovian gamification runs out of steam –
leaderboards, collecting badges and so on. Real gamers are intrinsically
motivated by the game, its reputation, their experiences of games, their peers
views of games and so on. They do not buy and play games because of the scoring
system or badges. Bad learning games or gamification techniques are often just
a pale imitation of massively popular gaming.

5. No form of
transfer

When your badges get stuck in a proprietary system, repository
or e-portfolio, with little in the way of interoperability, they’re effectively
imprisoned. Badges are often rendered useless by their failure to escape the
bounds of their small ecosystems, technical and cultural. Mozilla have, since
2011, tried to provide a framework and structure. I applaud their efforts but
the early paper “Open Badges for Lifelong Learning” was hopelessly utopian. A
more achievable vision was needed. The most successful badge system I’ve seen
is in IBM – but it is in IBM – that’s it. Badges don’t travel well.

6. Awful branding

Another problem was
branding. Making your badges look like silly, clip-art stickers, makes the
whole thing look amateurish. For badges to work they needed some serious
marketing and design – Mozilla tried but what we got was almost no marketing
and sometimes comically bad design. In addition, it always had that boy scout,
girl guide feel – something suitable for earnest young people but not adults. Perhaps
it was the word ‘badge’ that was a mistake – something with almost trivial
connotations.

7. Measurement

When people started to get badges for simply attending
conferences, I got worried. The motivation for conference attendance is not
always learning. It is often the extrinsic reward of travel and time off. How
do you measure the usefulness of that attendance? We could say, did you tweet
out session, blog and distribute your findings to your fellow employees, write
a paper suggesting new implementations based on what you learnt? Badges for just
turning up don’t wash it for me. A real problem here is that badges often don’t
match real learning and are rarely measured in terms of impact.

Conclusion

Foursquare and Gowalla allowed you to check-in, tag your
location and record what you did/are doing at those locations, through badges,
points, whatever. They were like a spiced-up Twitter, with points for prizes.
They died. Reduced to adding GIFs badges to Snapchat, they've had their day. Whether you see badges
as motivational devices, credentials, actual assessments, even evaluative, if
they don’t catch on, they’re dead in the water. In short, they’re dead in the
water. The truth is that this has happened, sad but true.

PS

There is one hope, a technology that avoids some of the problems
outlined here – Blockchain. I’ve written about this here…. Time will tell but
time is a cruel judge.

Sunday, February 18, 2018

Tyranny of time – why learning is a waste of time...

The learning world, at all levels, including offline and
online learning, suffers from an obsession that leads to massive waste and low
productivity – an obsession with time. This is why learning, rather than
increasing competence, performance and productivity, often exhibits failure, poor
performance and low productivity. The metrics almost universally cost ‘teaching
and learning’ like sausages…. by the pound/kilo - face time, contact time, fixed
length courses, and hour of learner time in online learning. All are metrics
that work against efficient delivery.

Higher Education

The one hour lecture, that pedagogic staple in HE, is an
hour long simply because the Sumerians had a base-60 number system -hence the ‘hour’. It bears no relation to the
psychology of attention or efficient pedagogy. It is quite simply the slavish
adherence to a fossilised method of delivery that is easy for faculty to
timetable. Even then, attendance is often appalling (even at Harvard), and often not recorded, rendering even rudimentary attempts at measurement
meaningless. University terms still adhere to an 18th century
agricultural calendar, with long holidays, that could have been designed as
periods of forgetting. Fixed three and four year length degree courses with
only one start date per year, take no account of actual needs. Oh, and lets
build and market ‘Masters’ Degrees to that we can add yet another year. Nowhere
is the tyranny of time more crude and obvious than in Higher Education.

Schools

Similarly in schools, that mimic Universities, as they must
be kept in sych, another form of tyranny as schools have been their feeders,
despite the fact that the majority of young people do not take that route. The
‘period’ in schools mimics the ‘lecture’ where millions of young people pack up,
stand up and shuffle through crowded corridors to another identical room where
they have to unpack, sit down and settle again. This waste of time is immense.
Imagine running a company where all employees have to rise on the hour and move
somewhere else? And again the tyranny of the agricultural calendar, where
unhealthy doses of forgetting punctuate the year, determine the rhythm of
learning, which should be stead, not full on, nothing, full on, nothing…..

Workplace

An obsession with ‘courses’ from compliance to whatever fad
arises (Emotional Intelligence, NLP. Mindfulness and so on) means days of
wasted time doing courses that have little or no effect on performance. Get people to travel from all over, then batch
people through in dull rooms with round tables, bowls of mints, coloured pens
and some half-baked attempt at collaboration, where you throw out a vague question,
discuss at the table, feedback on flipchart paper, which gets pinned on the
wall, then the promise that the results will be sent to you – they never are.
These courses are always delivered by the half-day, full day, or worse, days on
end and when it comes to impact the adherence to a ridiculous mode of evaluation (Kirkpatrick) means very little is meaningfully measured.

Online learning

Just as bad is online learning, bought and sold by the ‘learner
hour’, mimicking the University and school model. Rather then focus on value
and the idea that this really can save time, it encourages vendors to
over-deliver so that they can charge more. The net result is overdesigned
content, with oodles of meaningless, illustrative graphics, thinly punctuated
by multiple-choice questions, and maybe some Pavlovian gamification (so that a
premium price can be paid). Even MOOCs were foolishly deigned to match
University semesters, with a drip feed of content over up to 10 weeks – and
they wonder why people fell to the wayside?

What to do?

So the tyranny of time comes in many guises, the lecture,
period, semester, term, course and degree. Some make it worse by recommending
lifelong learning, in the form of going back to college – life as one long
courses. No thanks. Life is far too short for that nonsense. By and large all
of these take too long as they suffer from the following flaws:

1. Fixed form of
delivery

Most ‘teaching and learning’ is shaped by pre-existing,
fixed modes of delivery, the lecture, period, term, module, course and so on. This
‘ass before elbow’ mode of delivery should be shaped by the type of learning, needs
of learners and resources, not mode of delivery. The solution is to imagine
that the learning experience doesn’t exist, take it back to a blank slate, now
re-design. Match modes of delivery to the typology of learning, learning needs
and resources. Look to make everything shorter and more efficient for the
learner. Some call this Blended Learning - that doesn’t mean a bit of online
bolted on to a bit of classroom, let’s call that Velcro Learning, and don’t confuse Blended ‘Learning’ with Blended ‘Teaching’, where you simply slice and
dice a bit of your old and new delivery methods and call it a ‘Blend’. Escape
the tyranny of time and focus on value.

2. Sheep dip

Most teaching is a one-off event. It is ridiculous not to
record lectures, even if you think it’s a poor form of pedagogy (which I do).
Denying learners a second and third bite of the cherry is ridiculous – they may
be ill, miss points, not understand at first pass, have trouble note taking,
have the language of teaching as a second language. Above all the psychology of
learning shows that repeated access for reinforcement and retrieval through revision is necessary for efficient learning. There is a strong argument
for doing the same in schools. I’ve seen this work magnificently in an Italian school, yet few have ever thought about doing it.

3. Forgetting

Let’s not forget that single, fixed timetabled events ignore
a well known principle in learning – that the brain forgets almost everything
it’s taught. Ebbinghaus showed us this in 1885 and the learning world has
studiously ignored the principle that learners need, not repetition but
retrieval and deliberate practice. Learning needs to be repeatedly accessible,
say through recorded lectures right through to spaced practice techniques such
as top and tailing, note taking, repeated testing, up to algorithmically
determined, personalised deliberate practice. Deliberate, spaced-practice frees
learners from the tyranny of single event, sheep-dip learning.

4. Batching

Courses tend to batch learners who have to go through the linear
course at the same pace. In any group you will have a distribution curve, where
you only hit those in the middle. There will be tails of learners who find the
experience too slow or too fast. Personalised delivery, now possible through
adaptive, online learning, allows you to deliver learning to an individual,
informed by their progress and aggregated data from all who took the course
before. This results in increased attainment and lower dropout.

5. Less is more

In designing learning experiences, the ‘Garbage-In
Garbage-Out’ rule is not taken seriously enough. I’ve seen far too many long
compliance documents and over-engineered courses throw far too much detail at
learners. Lecturers pad out lectures to fit their ‘hour’. Course designers fill
out a timetable with unnecessary content and activities. The net result is
actually lower learning, retention and recall. Cognitive overload results in
less, not more, being retained. Research from large data sets has shown that
video in learning tends to fall of a cliff at around 6 minutes. The consequence
being that video should be that length or shorter.

The psychology of learning screams ‘less is more’ at us. Cut
down documents until they bleed then cut them down again, so that the content
is learning ready. There are few courses I’ve seen that can’t have up to 25-30%
cut out – all the padding. There is no doubt that lecturers pad out to the
hour, the same with classroom teachers and organisational trainers. Rather than
plan to fill the time, like an empty vessel that needs to be topped up, look at
making the learning experience as short as possible. Think about what learners
‘must’ learn, not generally what they ‘could’ learn. Of all the techniques to
free learners from the tyranny of time this one is by far the most productive.

6. Failure to chunk

Chunking is a pretty basic pie of learning theory – that our
working memory is limited and that throwing overlong learning experiences at
the learner is counter productive. It happens all the time. We teach people to
write essays by repeatedly getting them to ‘write essays’ rather than breaking
that task down into its constituent parts. Whole word teaching was an almost
perfect example of this approach to teaching that resulted in catastrophic
failure in reading in UK schools. Learning experiences have to have focus.

7. Digital by default

Rethinking learning around, not existing modes of delivery
and fixed timetables, but more flexible methods of delivery that suit the type
of learning, learners and resources is badly needed. More often than not this
means more 365/24/7 availability by being online. Being digital by default,
wherever is practical, turns time-tabled learning experiences into anytime
learning. Asynchronous often makes more sense than synchronous, even of its
recorded lectures and resources. Switch away from a dependence on courses to an
on-demand model.

Conclusion

In practice, as you get older and become a more self-sufficient
learner, you realise that freedom from the tyranny of time is the real trick to
learning. You literally ‘learn’ how to learn by being measured, having focus,
rehearsal, retrieval – by avoiding the waste of time that are courses and
degrees. That’s lifelong learning. Life is short, it's made even shorter by wasting so much time learning and not living.